In existing fields of car navigation, vehicle automatic driving, and driving safety tips, the most basic and important technology is a detecting technology about road traffic markings, such as lane lines and road signs. Currently, mainstream technical solutions mainly include the following two kinds. One solution is a detecting method based on changes of ground reflectivity: reflectivity of the road traffic markings is generally higher than reflectivity of other parts of a road surface. Therefore, a three-dimensional point cloud of a road scene space may first be obtained, and then the road traffic markings are extracted manually from the three-dimensional point cloud according to this feature. Because the solution directly detects and extracts the road traffic markings from the three-dimensional point cloud, a detecting result may be affected if a point cloud of the three-dimensional point cloud is sparse, occluded, missing, etc. Moreover, accuracy of the detecting result may be directly affected if the ground reflectivity turns out to be weak or uneven. The other solution is a manual detecting method: obtain a two-dimensional streetscape image and the three-dimensional point cloud of the road scene space, and use the two-dimensional streetscape image to apply colors to the three-dimensional point cloud, so as to generate a colorful point cloud, and then manually extract the road traffic markings from the colorful point cloud according to the two-dimensional streetscape image. The solution still directly extracts the road traffic markings from the three-dimensional point cloud, so it may be possible that the detecting result may be affected if the point cloud of the three-dimensional point cloud is sparse, occluded, missing, etc. Furthermore, manual detection is impractical and of low efficiency, easily causing mistakes.